Signature extraction using mutual interdependencies |
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Authors: | Heiko Claussen [Author Vitae] Justinian Rosca [Author Vitae] |
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Affiliation: | a Siemens Corporation, Corporate Research, 755 College Road East, Princeton, NJ 08540, USA b University of Southampton, School of Electronics and Computer Science, Highfield, Southampton SO17 1BJ, UK |
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Abstract: | Recently, mutual interdependence analysis (MIA) has been successfully used to extract representations, or “mutual features”, accounting for samples in the class. For example, a mutual feature is a face signature under varying illumination conditions or a speaker signature under varying channel conditions. A mutual feature is a linear regression that is equally correlated with all samples of the input class. Previous work discussed two equivalent definitions of this problem and a generalization of its solution called generalized MIA (GMIA). Moreover, it showed how mutual features can be computed and employed. This paper uses a parametrized version GMIA(λ) to pursue a deeper understanding of what GMIA features really represent. It defines a generative signal model that is used to interpret GMIA(λ) and visualize its difference to MIA, principal and independent component analysis. Finally, we analyze the effect of λ on the feature extraction performance of GMIA(λ) in two standard pattern recognition problems: illumination-independent face recognition and text-independent speaker verification. |
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Keywords: | Algorithms Signal processing Pattern classification Signal analysis Speaker recognition Face recognition |
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